New paper: SCOTTI Efficient reconstruction of transmission within outbreaks with the structured coalescent

New paper published today in PLoS Computational Biology: Understanding how infectious disease spreads and where it originates is essential for devising policies to prevent and limit outbreaks. Whole genome sequencing of pathogens has proved an extremely promising tool for identifying transmission, particularly when combined with classical epidemiological data. Several statistical and computational approaches are available for exploiting genomics for epidemiological investigation. These methods have seen applications to dozens of outbreak studies. However, they have a number of serious drawbacks.

In this new paper Nicola De Maio, Jessie Wu and I introduce SCOTTI, a method for quickly and accurately inferring who-infected- whom from genomic and epidemiological data. SCOTTI addresses very widespread, but generally neglected problems in joint epidemiological and genomic inference, notably the presence of non-sampled and undetected intermediate cases and within-host pathogen variation caused by microevolution. Using real examples and simulations, we show that these problems cause strong misleading effects on existing popular inference methods. SCOTTI is based on BASTA, our recent breakthrough method for phylogeographic inference, and offers new standards of accuracy, calibration, and computational efficiency. SCOTTI is distributed as an open source package within BEAST2.

Prize PhD Studentships available

I am offering two PhD projects as part of the annual Nuffield Department of Medicine Prize Studentship competition:

• Real-time detection of multidrug resistant tuberculosis and transmission in England
  Joint with David Wyllie, molecular microbiologist, this project is focused on developing statistical methods for recognizing transmission clusters, integrating genomics approaches with molecular typing schemes and developing future-proof taxonomy for strain identification.
  
  
• Tracking future infection threats using genomic data and electronic health records
  Joint with David Clifton, biomedical engineer, this project aims to develop new machine learning and statistical methods to identify genomic markers of antibiotic resistance and susceptibility within various pathogens, to help track future infection threats.

These are fully-funded, four-year awards open to outstanding students of any nationality. Applicants nominate three projects, in order of preference, from the available pool. For how to apply, click here. Only applications submitted through the online system will be considered, but interested applicants are welcome to contact me informally. The deadline for applications is noon, 6th January 2017.

In addition to my projects, the Modernising Medical Microbiology project has announced the following PhD projects as part of the competition:

• Antimicrobial resistance gene/vector transmission across human, animal and environmental reservoirs
  Supervised by Nicole Stoesser, Nicola De Maio and Derrick Crook
  
  
• Healthcare big data and genomics for infectious disease threat detection
  Supervised by David Clifton, David Eyre and Tim Peto
  
  
• Prediction of Mycobacterium tuberculosis drug resistance through genome sequencing clinical samples
  Supervised by Tim Walker and Tim Peto
  
  
• Antibiotic resistance in Tuberculosis: Predicting de novo the effect of individual genetic mutations
  Supervised by Phil Fowler and Sarah Walker